AGW manifestations in the Earth neutral atmosphere and ionosphere

2021 ◽  
Author(s):  
Tatiana Syrenova ◽  
Alexander Beletsky
Keyword(s):  
Optical System ◽  
Deep Convection ◽  
Lower Atmosphere ◽  
Automatic Identification ◽  
Complex Data ◽  
Neutral Atmosphere ◽  
Data Set ◽  
Acoustic Gravity Waves ◽  
Wave Disturbances ◽  
System Data

<p>Acoustic gravity waves (AGW) manifestations spread from the lower atmosphere to the upper layers due to processes such as orography, weather fronts, deep convection atmosphere, and vice versa, can form in the upper atmosphere during geomagnetic activity, receiving energy from the magnetosphere. These wave processes can be considered as a dynamic process that transfers energy between different atmospheric and latitudinal regions, therefore it is important to understand their basic parameters and behavior.</p><p>In this work, to study wave disturbances, we used the Keo Sentinel optical system data, designed to record the spatial pattern of the 630 nm emission intensity (emission height 180-300 km). The system is located at the Geophysical Observatory (GPO) of the ISTP SB RAS, near the Tory, Buryatiya, Russia (52<sup>0</sup> N, 103<sup>0</sup> E, height 670 m). The  interference filter transmission half-width is ~ 2 nm. Sight direction - zenith, field of view 145 degrees, exposure time 30-60 s (http://atmos.iszf.irk.ru/ru/data/keo).</p><p>For the analysis, we chose data obtained on clear, moonless nights from 2014 to March 2019. The total number of nights selected for analysis was 71 (~ 491 hours). An algorithm for the wave events and their characteristics automatic identification from the optic data was developed and tested. The approbation was carried out on a data set previously processed manually [Syrenova, Beletsky, 2019]. A comparison was made with traveling ionospheric disturbances (TID) characteristics obtained from the ISTP SB RAS radio-physical complex data [Medvedev et al., 2012].</p><p>The main directions of wave disturbances propagation obtained with automatic optical system data processing - southward (~ 175º) and eastward (~ 90º) - are similar to the TID directions. From the radiophysical complex data, the TID distribution from North to South prevails, the most probable azimuth is ~ 135º during the day, and ~ 205º at night. The most probable values ​​of the wave disturbances propagation velocity obtained as a result of automatic processing are about 80 m/s. These values ​​also accept well with the TID values.</p><p>The main characteristics obtained using the data of the optical and radiophysical complexes agree with each other. Differences in the preferred propagation direction of the recorded wave structures from the KEO Sentinel data from the directions obtained with photometers at the same observation point [Tashchilin, 2010, Podlesny, 2018], probably, associated with different observation heights.</p>

scholarly journals Wave over the source in a thermal-conductive atmosphere

2015 ◽  
Vol 1 (4) ◽  
pp. 11-29 ◽  
Author(s):  
Георгий Руденко ◽  
Georgiy Rudenko ◽  
Ирина Дмитриенко ◽  
Irina Dmitrienko
Keyword(s):  
Thermal Conductivity ◽  
Analytical Solution ◽  
Numerical Solution ◽  
Gravity Waves ◽  
Lower Atmosphere ◽  
The Real ◽  
Acoustic Gravity Waves ◽  
Wave Disturbances ◽  
Small Dissipation

For acoustic-gravity waves, we propose a method for obtaining solutions over the source, taking into account the thermal conductivity throughout the atmosphere. The solution is constructed by combining the analytical solution for the upper isothermal part and numerical solution for the real non-isothermal dissipative atmosphere. The possibility of different ways of describing the wave disturbances investigated for different altitudinal ranges. A special way of accounting for small dissipation of the lower atmosphere is proposed. The heights of strong dissipation are found.

scholarly journals Overview and summary of the Spread F Experiment (SpreadFEx)

2009 ◽  
Vol 27 (5) ◽  
pp. 2141-2155 ◽  
Author(s):  
D. C. Fritts ◽  
M. A. Abdu ◽  
B. R. Batista ◽  
I. S. Batista ◽  
P. P. Batista ◽  
...  
Keyword(s):  
Gravity Wave ◽  
Spatial Scales ◽  
Deep Convection ◽  
Lower Atmosphere ◽  
Temperature Fields ◽  
Small Scale ◽  
Neutral Atmosphere ◽  
Equatorial Spread F ◽  
Plasma Bubbles ◽  
Spread F

Abstract. We provide here an overview of, and a summary of results arising from, an extensive experimental campaign (the Spread F Experiment, or SpreadFEx) performed from September to November 2005, with primary measurements in Brazil. The motivation was to define the potential role of neutral atmosphere dynamics, specifically gravity wave motions propagating upward from the lower atmosphere, in seeding Rayleigh-Taylor instability (RTI) and plasma bubbles extending to higher altitudes. Campaign measurements focused on the Brazilian sector and included ground-based optical, radar, digisonde, and GPS measurements at a number of fixed and temporary sites. Related data on convection and plasma bubble structures were also collected by GOES 12, and the GUVI instrument aboard the TIMED satellite. Initial results of our SpreadFEx analyses are described separately by Fritts et al. (2009). Further analyses of these data provide additional evidence of 1) gravity wave (GW) activity near the mesopause apparently linked to deep convection predominantly to the west of our measurement sites, 2) small-scale GWs largely confined to lower altitudes, 3) larger-scale GWs apparently penetrating to much higher altitudes, 4) substantial GW amplitudes implied by digisonde electron densities, and 5) apparent influences of these perturbations in the lower F-region on the formation of equatorial spread F, RTI, and plasma bubbles extending to much higher altitudes. Other efforts with SpreadFEx data have also yielded 6) the occurrence, locations, and scales of deep convection, 7) the spatial and temporal evolutions of plasma bubbles, 8) 2-D (height-resolved) structures in electron density fluctuations and equatorial spread F at lower altitudes and plasma bubbles above, and 9) the occurrence of substantial tidal perturbations to the large-scale wind and temperature fields extending to bottomside F-layer and higher altitudes. Collectively, our various SpreadFEx analyses suggest direct links between deep tropical convection and large GW perturbations at large spatial scales at the bottomside F-layer and their likely contributions to the excitation of RTI and plasma bubbles extending to much higher altitudes.

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

2021 ◽  
pp. 1-22
Author(s):  
Lei Jinyu ◽  
Liu Lei ◽  
Chu Xiumin ◽  
He Wei ◽  
Liu Xinglong ◽  
...  
Keyword(s):  
Least Squares Method ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Automatic Identification System ◽  
Domain Models ◽  
System Data ◽  
Quantification Model ◽  
Highly Correlated

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

scholarly journals 136. Don’t Sweat the Small Stuff: Solutions for Large-Scale Stewardship Obstacles

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S79-S80
Author(s):  
Joanne Huang ◽  
Zahra Kassamali Escobar ◽  
Rupali Jain ◽  
Jeannie D Chan ◽  
John B Lynch ◽  
...  
Keyword(s):  
Urgent Care ◽  
Complex Data ◽  
Open Communication ◽  
University Of Washington ◽  
Data Set ◽  
Constructive Feedback ◽  
Diagnostic Coding ◽  
Icd 10 ◽  
Tract Infections ◽  
The University

Abstract Background In an effort to support stewardship endeavors, the MITIGATE (a Multifaceted Intervention to Improve Prescribing for Acute Respiratory Infection for Adult and Children in Emergency Department and Urgent Care Settings) Toolkit was published in 2018, aiming to reduce unnecessary antibiotics for viral respiratory tract infections (RTIs). At the University of Washington, we have incorporated strategies from this toolkit at our urgent care clinics. This study aims to address solutions to some of the challenges we experienced. Challenges and Solutions Methods This was a retrospective observational study conducted at Valley Medical Center (Sept 2019-Mar 2020) and the University of Washington (Jan 2019-Feb 2020) urgent care clinics. Patients were identified through ICD-10 diagnosis codes included in the MITIGATE toolkit. The primary outcome was identifying challenges and solutions developed during this process. Results We encountered five challenges during our roll-out of MITIGATE. First, using both ICD-9 and ICD-10 codes can lead to inaccurate data collection. Second, technical support for coding a complex data set is essential and should be accounted for prior to beginning stewardship interventions of this scale. Third, unintentional incorrect diagnosis selection was common and may require reeducation of prescribers on proper selection. Fourth, focusing on singular issues rather than multiple outcomes is more feasible and can offer several opportunities for stewardship interventions. Lastly, changing prescribing behavior can cause unintended tension during implementation. Modifying benchmarks measured, allowing for bi-directional feedback, and identifying provider champions can help maintain open communication. Conclusion Resources such as the MITIGATE toolkit are helpful to implement standardized data driven stewardship interventions. We have experienced some challenges including a complex data build, errors with diagnostic coding, providing constructive feedback while maintaining positive stewardship relationships, and choosing feasible outcomes to measure. We present solutions to these challenges with the aim to provide guidance to those who are considering using this toolkit for outpatient stewardship interventions. Disclosures All Authors: No reported disclosures

scholarly journals DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity

2021 ◽  
Vol 15 ◽  
pp. 117793222110303
Author(s):  
Asad Ahmed ◽  
Bhavika Mam ◽  
Ramanathan Sowdhamini
Keyword(s):  
Deep Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Chemical Space ◽  
Biological Significance ◽  
Protein Crystal ◽  
Ligand Docking ◽  
Complex Data ◽  
Binding Prediction ◽  
Data Set

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.

Automatic Identification of Roadway Horizontal Alignment Information Using Geographic Information System Data: CurvS Tool

2021 ◽  
pp. 036119812110363
Author(s):  
Bekir Bartin ◽  
Sami Demiroluk ◽  
Kaan Ozbay ◽  
Mojibulrahman Jami
Keyword(s):  
Information System ◽  
New Jersey ◽  
Geographic Information System ◽  
Geographic Information ◽  
Automatic Identification ◽  
Web Based ◽  
Horizontal Alignment ◽  
System Data ◽  
Overlap Ratio ◽  
Geographic Information System Data

This paper introduces CurvS, a web-based tool for researchers and analysts that automatically extracts, visualizes, and analyzes roadway horizontal alignment information using readily available geographic information system roadway centerline data. The functionalities of CurvS are presented along with a brief background on its methodology. The validation of its estimation results are presented using actual horizontal alignment data from two different roadway types: Route 83, a two-lane two-way rural roadway in New Jersey and I-80, a freeway segment in Nevada. Different metrics are used for validation. These are identification rates of curved and tangent sections, overlap ratio of curved and tangent sections between estimated and actual horizontal alignment data, and percent fit of curve radii. The validation results show that CurvS is able to identify all the curves on these two roadways, and the estimated section lengths are significantly close to the actual alignment data, especially for the I-80 freeway segment, where 90% of curved length and 94% of tangent section length are correctly matched. Even when curves have small central angles, such as the ones in Route 83, CurvS’s estimations covers 71% of curved length and 96% of tangent section length.

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